Update of Canadian Historical Snow Survey Data and Analysis of Snow Water Equivalent Trends, 1967–2016
Bibliographic record
Abstract
In situ observations of snow water equivalent (SWE) from manual snow surveys and automated sensors are made at approximately 1000 sites across Canada in support of water resource planning for flood control and hydroelectricity production. These data represent an important source of information for research (e.g., validation of hydrological and climate models), for applied studies (e.g., ground snow loads), and for climate monitoring. This note describes the process to update a Canadian historical snow survey dataset to 2016 and the production of a 0.1° gridded version for research applications. Analysis of trends in SWE, snow depth (SD), and density over the 50-year period from 1967 to 2016 revealed large spatial variability in trend sign and strength, with a relatively small percentage of points showing statistically significant trends. Where SWE and SD trends were significant, they tended to be negative, which is consistent with previous investigations of snow cover changes in Canada. The results show evidence of a latitudinal dependence in SWE trends, with the largest negative trends occurring over lower latitudes, and a tendency for mainly positive trends in Arctic SWE, which is consistent with observations from Russia and climate model projections of the response of Arctic snow cover to climate warming. Arctic sites also showed evidence of an increasing trend in 1 April snowpack density of 6.6 kg m−3 per decade but little corresponding change in SD. This has potentially important consequences for the soil thermal regime because it provides a cooling influence from an increase in the snowpack effective thermal conductivity. The snow survey dataset is available from the Government of Canada Open Data portal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".